Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment

This work explores the role of steady-state dynamic analysis in the vibration-based structural health monitoring field. While more traditional approaches focus on transient or stochastic vibration analysis, the method described here utilizes a geometric portrait of system dynamics to extract information about the steady-state response of the structure to sustained excitation. The approach utilizes the fundamental properties of chaotic signals to produce low-dimensional response data which are then analyzed for features which indicate the degree to which the dynamics have been altered by damage. A discussion of the fundamental issues involved in the approach is presented along with experimental evidence of the approach's ability to discriminate among several damage scenarios.